The resting state fMRI time series appears to have cyclic patterns, which indicates presence of cyclic interactions between different brain regions. Such interactions are not easily captured by pre-established resting state functional connectivity methods including zero-lag correlation, lagged correlation, and dynamic time warping distance. These methods formulate the functional interaction between different brain regions as similar temporal patterns within the time series. To use information related to temporal ordering, cyclicity analysis has been introduced to capture pairwise interactions between multiple time series. In this study, we compared the efficacy of cyclicity analysis with aforementioned similarity-based techniques in representing individual-level and group-level information. Additionally, we investigated how filtering and global signal regression interacted with these techniques. We obtained and analyzed fMRI data from patients with tinnitus and neurotypical controls at two different days, a week apart. For both patient and control groups, we found that the features generated by cyclicity and correlation (zero-lag and lagged) analyses were more reliable than the features generated by dynamic time warping distance in identifying individuals across visits. The reliability of all features, except those generated by dynamic time warping, improved as the global signal was regressed. Nevertheless, removing fluctuations >0.1 Hz deteriorated the reliability of all features. These observations underscore the importance of choosing appropriate preprocessing steps while evaluating different analytical methods in describing resting state functional interactivity. Further, using different machine learning techniques including support vector machines, discriminant analyses, and convolutional neural networks, our results revealed that the manifestation of the group-level information within all features was not sufficient enough to dissociate tinnitus patients from controls with high sensitivity and specificity. This necessitates further investigation regarding the representation of group-level information within different features to better identify tinnitus-related alternation in the functional organization of the brain. Our study adds to the growing body of research on developing diagnostic tools to identify neurological disorders, such as tinnitus, using resting state fMRI data.

中文翻译：

---

将循环分析与预先建立的功能连接方法进行比较，以使用静息状态 fMRI 识别个人和受试者组

静息状态 fMRI 时间序列似乎具有循环模式，这表明不同大脑区域之间存在循环相互作用。这种相互作用不容易被预先建立的静息状态功能连接方法捕获，包括零滞后相关、滞后相关和动态时间扭曲距离。这些方法将不同大脑区域之间的功能相互作用制定为时间序列内的相似时间模式。为了使用与时间排序相关的信息，引入了循环分析来捕获多个时间序列之间的成对交互。在这项研究中，我们比较了周期性分析与上述基于相似性的技术在表示个人级别和组级别信息方面的功效。此外，我们研究了滤波和全局信号回归如何与这些技术相互作用。我们在相隔一周的两天不同的日子里，从耳鸣患者和神经典型对照组中获取并分析了 fMRI 数据。对于患者组和对照组，我们发现循环性和相关性（零滞后和滞后）分析生成的特征比动态时间扭曲距离生成的特征在跨访问识别个体时更可靠。除了动态时间扭曲产生的特征之外，所有特征的可靠性都随着全局信号的​​回归而提高。然而，消除大于 0.1 Hz 的波动会降低所有特征的可靠性。这些观察结果强调了选择适当的预处理步骤的重要性，同时评估不同的分析方法来描述静息状态功能交互性。此外，使用不同的机器学习技术，包括支持向量机、判别分析和卷积神经网络，我们的结果表明，所有特征中的组级信息的表现不足以将耳鸣患者与具有高灵敏度和特异性的对照区分开来。 . 这需要进一步研究不同特征中组级信息的表示，以更好地识别大脑功能组织中与耳鸣相关的交替。